technical paper

AAAI 2024

February 25, 2024

Vancouver , Canada

SuperJunction: Learning-Based Junction Detection for Retinal Image Registration | VIDEO


retinal image

image registration

machine learning

Keypoints-based approaches have shown to be promising for retinal image registration, which superimpose two or more images from different views based on keypoint detection and description. However, existing approaches suffer from ineffective keypoint detector and descriptor training. Meanwhile, the non-linear mapping from 3D retinal structure to 2D images is often neglected. In this paper, we propose a novel learning-based junction detection approach for retinal image registration, which enhances both the keypoint detector and descriptor training. To improve the keypoint detection, it uses a multi-task vessel detection to regularize the model training, which helps to learn more representative features and reduce the risk of over-fitting. To achieve effective training for keypoints description, a new constrained negative sampling approach is proposed to compute the descriptor loss. Moreover, we also consider the non-linearity between retinal images from different views during matching. Experimental results on FIRE dataset show that our method achieves mean area under curve of 0.850, which is 12.6% higher than 0.755 by the state-of-the-art method. All the codes are available at


SlidesPaperTranscript English (automatic)

Next from AAAI 2024

When Do Program-of-Thought Works for Reasoning?
technical paper

When Do Program-of-Thought Works for Reasoning?

AAAI 2024

+3Shumin DengHuajun ChenZhen Bi
Zhen Bi and 5 other authors

25 February 2024

Stay up to date with the latest Underline news!

Select topic of interest (you can select more than one)


  • All Lectures
  • For Librarians
  • Resource Center
  • Free Trial
Underline Science, Inc.
1216 Broadway, 2nd Floor, New York, NY 10001, USA

© 2023 Underline - All rights reserved